Free ticketed event
The use of artificial intelligence (AI) based applications is increasing in all engineering disciplines. Higher education must keep pace with these developments and leverage them to conduct better research and training and, critically, ensure that students are prepared to use these tools in their work and for lifelong learning.
In particular, in recent years, the use of generative AI (GAI)-driven tools and applications such as ChatGPT, Dall-E, Midjourney, and CoPilotand Autodesk has become popular. GAI is a subfield of AI that uses deep learning and large language models to generate new content. Generative writing is being used to create copy, write job descriptions, and provide technical documentation. Generative design systems allow engineers to start with pre-designed models. Generation of code-based on a prompt is making the software development process more efficient.
This workshop will focus on several aspects of using GAI, including:
(1) Research: Data Generation, Data Analysis, Data Reporting, Instrument Creation, Data Presentation, and Paper Drafting; and
(2) Teaching: Assessment, Question Creation, Assignment Generation, Preparation for Teaching, Syllabi Generation, Topic/Concept Generation, and Topic Summarization.
Participants will work through some in-depth scenarios and discuss ethical issues related to the use of AI and GAI, and how to use these applications in a more responsible manner.
Attendees will be invited to create accounts on different GAI application sites to experience them firsthand. A list of tools and applications will enable them to continue to explore features and evaluate the applications' usefulness for their research and teaching practices.
The speakers are currently PIs on separate NSF grants related to this topic and this workshop builds on that research.
Aditya Johri is Professor of Information Sciences & Technology and Director of Technocritical Research in AI, Learning & Soceity Lab (trailsLAB) at George Mason University, USA. He studies how technology shapes learning across formal and informal settings and the ethical implications of using technology in education. He publishes broadly in the fields of engineering and computing education, educational technology, and computer-supported collaborative work and learning. His research has been recognized with several best paper awards and his co-edited volume, the Cambridge Handbook of Engineering Education Research (CHEER), received the 2015 Best Book Publication Award from Division I of AERA. Most recently he served as a Fulbright-Nokia Distinguished Chair in ICT at Aalto University, Finland (2021). He is a past receipient of the NSF Early Career Award and in 2022 University Teaching Excellence Award and Mentoring Excellence Award for undergraduate research at George Mason University. He was awarded a Ph.D. in Learning Sciences & Technology Design (2007) from Stanford University, Palo Alto, CA. More information is available at: http://mason.gmu.edu/~johri
Andrew Katz is an assistant professor in the Department of Engineering Education at Virginia Tech. He received his Ph.D. in engineering education from Purdue University, has a master’s degree in environmental engineering from Texas A&M University and a bachelor’s degree in chemical engineering from Tulane University. His research focuses on engineering ethics, decision-making, and system development. To do this, he examines topics such as faculty mental models of engineering ethics and education, processes of change in ethics education, and students’ views of ethics and social responsibility. Three particular, current areas of interest in the lab are: environmental sustainability (i.e., how students learn and make decisions that affect the environment), automated technologies (i.e., how educators can use digital technologies as well as how engineers make design decisions for automated technologies that affect communities), and human health (i.e., how we can make sure education systems promote a holistic student formation that foster mental and physical health, minimizing the stereotypical grind associated with pursuing an undergraduate engineering degree).